News-Topic Oriented Hashtag Recommendation in Twitter Based on Characteristic Co-occurrence Word Detection

Hashtags, which started to be widely used since 2007, are always utilized to mark keywords in tweets to categorize messages and form conversation for topics in Twitter. However, it is hard for users to use hashtags for sharing their opinions/interests/comments for their interesting topics. In this paper, we present a new approach for recommending news-topic oriented hashtags to help Twitter users easily join the conversation about news topics in Twitter. We first detect topic-specific informative words co-occurring with a given target word, which we call characteristic co-occurrence words, from news articles to form a vector for representing the news topic. Then by creating a hashtag vector based on tweets with the same hashtag, we calculate the similarity between these two vectors and recommend hashtags of high similarity scores with the news topic. Experimental results show that our approach could recommend hashtags which are highly relevant to the news topics, helping users share their tweets with others in Twitter.